Dramatic changes in industry have exposed the need for new processes and tools to measure, analyze and manage credit and liquidity. For example, energy companies have been reeling from corporate scandals, increased scrutiny and disclosure, and several well-publicized bankruptcies. As a result, companies are planning for contingent liquidity requirements and managing company-wide credit by requiring near-real-time profiles of the company's credit exposure and obligations. Some companies do business with hundreds of different counterparties and, therefore, have risk associated with hundreds of different legal entities based on a myriad of different commodities. The data about these counterparties and the transactions executed with them is spread across many different specialized commodity-trading systems.
Unfortunately, the commodity-based nature of enterprise resource planning, integration, trading and risk management software currently used in most industries revolve around accounts and transactions as opposed to customer relationships or counterparties and their associated contracts. This places the relevant data scattered across multiple, disparate systems and forces company executives to manually pull together necessary information and resort to spreadsheets and calculators to obtain the information they need to assess credit risk and make critical decisions regarding their company's credit exposure and obligations. An organization's financial stability depends on a timely, accurate and authoritative picture of credit exposure and liquidity obligations, so it may identify trouble spots, move quickly to mitigate counterparty credit risk, and improve the company's liquidity. This critical information must be made available to organizations by presenting a comprehensive, detailed, real-time picture of current exposure and collateral requirements for both the company and its counterparties. Yet, no current solution provides an effective method to track and analyze credit exposure and liquidity obligations across multiple systems. In addition to data aggregation limitations, the current offerings do not take advantage of contemporary technologies that allow for simplified adaptation of changing functional requirements and near-real-time processing. Accordingly, it would be desirable to provide a method, system and program to overcome these problems in the art.